Building Production-Ready AI Agents: Overcoming the Prototype-to-Product Gap

As AI agents move from research projects to production systems, developers face critical challenges: unpredictable behavior, execution loops, and complex retrieval systems that fail in the field. This article advocates for deterministic, state-machine-based architectures and structured prompt engineering over informal coding practices—directly addressing why production AI agents behave differently than prototypes. These practices matter because they determine whether an agent is a reliable business tool or an expensive operational liability.